Emotion Sensors Go To School

By Ivon Arroyo, David G. Cooper, Winslow Burleson, Beverly Park Woolf, Kasia Muldner, Robert Christopherson. International Conference on Artificial Intelligence in Education, 2009

431 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/37713e06a7dbd12a73e692f3690990024422aada

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Citations in the corpus

A Systematic Review of Empirical Studies on Learning Analytics Dashboards: A Self-Regulated Learning Perspective (2020, methodology): “The TEA model was proposed by [39] which was informed by the work of Pekrun, Goetz, Frenzel, Barchfeld, & Perry [40] and Arroyo et al. [41].”
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications (2010, background): “Fortunately, some of the challenges associated with deploying intrusive physiological sensing devices in real-world contexts are being mitigated by recent advances in the design of wearable sensors (e.g., [35]).”
A Review of Artificial Intelligence (AI) in Education from 2010 to 2020 (2021, methodology): “Based on the maturity of biofeedback technique, such as eye-tracking and EEG, aﬀection computing was increasingly adopted to investigate students' internal motivations on learning, such as creativity and responsibility [49, 50].”
Artificial Intelligence in Education: 20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019, Proceedings, Part II (2019, methodology): “Importantly, speed dating can often reveal needs and opportunities that may not be observed through field observations or other design activities [12,13,42,67].”
Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features (2010, background): “Their results also indicated that a combination of facial features and contextual cues yielded the best models to predict confidence, excited, and interested (Arroyo et al. 2009).”
Affect-aware tutors: recognising and responding to student affect (2009, methodology): “Pre-production prototypes of each sensor were developed and 25 sets manufactured for simultaneous use in classrooms in Fall 2008 (Cooper et al., 2009; Arroyo et al., 2009a–b).”
AutoTutor and affective autotutor: Learning by talking with cognitively and emotionally intelligent computers that talk back (2012, background): “Some of these include pressure mats that are constructed in-house [Arroyo et al. 2009], Wii Fit Balance BoardsTM [Olney and D Mello 2010], and motion .ltering techniques to assess body movements from live video [D Mello et al. 2012].”
A review of recent advances in learner and skill modeling in intelligent learning environments (2012, background): “Arroyo et al. (2009) have created a relatively inexpensive suite of sensors (webcam, conductance bracelet , pressure mouse, posture analysis seat), and have succeeded in deploying detectors of confidence, frustration, excitement, and interest to entire classrooms at one time.”
Mood measurement with Pick-A-Mood: review of current methods and design of a pictorial self-report scale (2016, background): “Moreover, modern affective wearables (like wrist bands) are inexpensive, unobtrusive, and allow for natural interaction, because they can be used in natural settings (e.g. Arroyo et al., 2009).”
Learner Engagement in Blended Learning Environments: A Conceptual Framework (2019, background): “But confidence may also indicate engagement: Self-reports of confidence “depen[d] on events that occurred in [solving] the previous problem and not on [learners’] incoming beliefs” (Arroyo et al., 2009, p. 19). Thus, subsequent testing of this model might frame items to measure not only learners’ ge”